Add retries in case the embeddings API fails (#628)

* Add retries in case the embeddings API fails
* Improve error handling in the inference endpoint API request handler
- retry only if HTTP exception
- use logger to output information about errors

src/khoj/processor/embeddings.py

@@ -4,6 +4,13 @@ from typing import List
 import requests
 import tqdm
 from sentence_transformers import CrossEncoder, SentenceTransformer
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_random_exponential,
+)
 from torch import nn
 
 from khoj.utils.helpers import get_device
@@ -28,13 +35,32 @@ class EmbeddingsModel:
 
     def embed_query(self, query):
         if self.api_key is not None and self.inference_endpoint is not None:
-            target_url = f"{self.inference_endpoint}"
-            payload = {"inputs": [query]}
-            headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
-            response = requests.post(target_url, json=payload, headers=headers)
-            return response.json()["embeddings"][0]
+            return self.embed_with_api([query])[0]
         return self.embeddings_model.encode([query], show_progress_bar=False, **self.encode_kwargs)[0]
 
+    @retry(
+        retry=retry_if_exception_type(requests.exceptions.HTTPError),
+        wait=wait_random_exponential(multiplier=1, max=10),
+        stop=stop_after_attempt(5),
+        before_sleep=before_sleep_log(logger, logging.DEBUG),
+    )
+    def embed_with_api(self, docs):
+        payload = {"inputs": docs}
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        try:
+            response = requests.post(self.inference_endpoint, json=payload, headers=headers)
+            response.raise_for_status()
+        except requests.exceptions.HTTPError as e:
+            logger.error(
+                f" Error while calling inference endpoint {self.inference_endpoint} with error {e}, response {response.json()} ",
+                exc_info=True,
+            )
+            raise e
+        return response.json()["embeddings"]
+
     def embed_documents(self, docs):
         if self.api_key is not None and self.inference_endpoint is not None:
             target_url = f"{self.inference_endpoint}"
@@ -44,25 +70,12 @@ class EmbeddingsModel:
                 )
                 return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()
             # break up the docs payload in chunks of 1000 to avoid hitting rate limits
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json",
-            }
             embeddings = []
             with tqdm.tqdm(total=len(docs)) as pbar:
                 for i in range(0, len(docs), 1000):
-                    payload = {"inputs": docs[i : i + 1000]}
-                    response = requests.post(target_url, json=payload, headers=headers)
-                    try:
-                        response.raise_for_status()
-                    except requests.exceptions.HTTPError as e:
-                        print(f"Error: {e}")
-                        print(f"Response: {response.json()}")
-                        raise e
-                    if i == 0:
-                        embeddings = response.json()["embeddings"]
-                    else:
-                        embeddings += response.json()["embeddings"]
+                    docs_to_embed = docs[i : i + 1000]
+                    generated_embeddings = self.embed_with_api(docs_to_embed)
+                    embeddings += generated_embeddings
                     pbar.update(1000)
             return embeddings
         return self.embeddings_model.encode(docs, show_progress_bar=True, **self.encode_kwargs).tolist()